Optimizing Classification Performance with Decision Lists under Constraints

Music City Center 

Logic rules combining OR-AND threshold operations are widely used in cancer early detection due to their simplicity and interpretability. These rules are part of efforts to develop biomarker panels that optimize performance under constraints like maintaining high specificity while maximizing sensitivity. However, traditional approaches like classification trees (CART) often fail to meet such constraints despite their predictive accuracy. We propose a novel method using decision lists—sequential if-then rules that map covariates to outcomes—to develop parsimonious combinatory threshold rules for biomarkers. This method allows for sequential biomarker measurement, reducing the need for further tests if initial conditions are met, thus decreasing patient and specimen burden. Our simulations and application to pancreatic cancer data demonstrate the method's superiority in maintaining constrained optimization over comparative approaches.

Logic rule

decision list

cancer biomarker

constrained optimization

classification